---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-101-finetuned_resnet101-cnn-autotags
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9228571428571428
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet-101-finetuned_resnet101-cnn-autotags

This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2099
- Accuracy: 0.9229

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0434        | 0.99  | 65   | 1.6045          | 0.4829   |
| 0.9013        | 1.99  | 130  | 0.6946          | 0.7648   |
| 0.7097        | 2.99  | 195  | 0.4928          | 0.8295   |
| 0.4386        | 3.99  | 260  | 0.3632          | 0.8610   |
| 0.4261        | 4.99  | 325  | 0.3269          | 0.8838   |
| 0.3181        | 5.99  | 390  | 0.2790          | 0.9010   |
| 0.2349        | 6.99  | 455  | 0.2377          | 0.9190   |
| 0.1615        | 7.99  | 520  | 0.2416          | 0.9114   |
| 0.1146        | 8.99  | 585  | 0.2162          | 0.9219   |
| 0.1254        | 9.99  | 650  | 0.2099          | 0.9229   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2